Expert Point of View: Kelly M. McMasters, MD, PhD

Their effort represents the first attempt to use ensemble learning to predict colorectal cancer recurrence. This will likely lead to better molecular staging tests for colorectal cancer in the future.

— Kelly M. McMasters, MD, PhD

“Several gene-expression profiles have been evaluated to predict prognosis in colorectal cancer, but none have become widely accepted or U.S. Food and Drug Administration–approved. This has important implications for deciding which patients may benefit from adjuvant chemotherapy,” commented session moderator Kelly M. McMasters, MD, PhD, a surgical oncologist with the University of Louisville School of Medicine, in Kentucky.

“Dr. Castellanos and colleagues from Vanderbilt evaluated the complex bioinformatics approach of ensemble learning to evaluate gene-expression data from six publicly available microarray data sets and develop more robust prognostic models,” he said.

“Their effort represents the first attempt to use ensemble learning to predict colorectal cancer recurrence. This will likely lead to better molecular staging tests for colorectal cancer in the future, which will allow more personalized treatment decisions of patients with this disease,” Dr. McMasters concluded. ■

Disclosure:Dr. McMasters reported no potential conflicts of interest.

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A machine learning model that uses a set or ensemble of algorithms has good accuracy for predicting colorectal cancer recurrence, investigators reported during a plenary session at the 2017 Society of Surgical Oncology (SSO) Annual Cancer Symposium.1